The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model’s robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures.

Crack detection is one of the crucial aspects of bridge evaluation and maintenance. Several existing image-based methods require capturing the bridge surface and extracting crack features to detect the crack. However, in some positions such as the space under the bridge and piers, it is difficult to capture crack images. This paper aims to apply a method to detect cracks on the bridge surface by using a drone that can capture images in challenging positions. The video recorded from the drone will be automatically identified the cracks by employing the deep learning method. Deep learning is designed for training and testing the dataset with 51.000 images, each image sized 244 × 244. The deep learning method shows the feasibility of detecting the cracks in the transport facility. This is supported by the high accuracy of the experimental results of 95.19%. In addition, the tool can assign an ID containing information to each crack from video so that these cracks can then be mounted on a 3D map of the bridge for research on crack development over time in the task of assessing the health of bridges.

This study contributes an efficient method for assessing the structural integrity of concrete bridges by collecting images of surface cracks and defects with drones. The use of drones to gather crack samples reduces human error and is appropriate in adverse working conditions and short-duration examinations. This work introduces a new POI (Points of Interest) flying algorithm for typical flight paths, based on the drone flying criteria of safe viewing distance and the POI concept. In this research, the flight controller of the drone was combined with a Raspberry Pi4 to build and alter a typical flight path suitable for the mission, with the addition of interactive features that control flight mode switching, separation, and automatic return to the original orbit to meet expertly specified POIs. In other words, as drones approach a region requiring image capture, they automatically move to a safe distance to photograph cracks and then return to their predetermined orbit. An experiment at the Cau May bridge demonstrates that the path-planning algorithms created on an embedded computer can successfully coordinate with the flight controller switching three times to hover and zoom in on fractures during the inspection.

This study presents a low-cost edge-IoT-based structural health monitoring (SHM) architecture for crack monitoring in operational underground tunnels. The system integrates crack-displacement sensing, temperature measurement, ESP32-based edge processing, LoRa/MQTT communication, AES/ECDSA-based data protection, and cloud-based data management. The architecture was validated through a 30-day field campaign at two representative cracks in the Hai Van Tunnel, with measurements acquired at 60 s sampling intervals. The results show that edge-based wavelet–Kalman processing improved measurement stability and reduced high-frequency noise, while the implemented security mechanism introduced only minor latency relative to the monitoring cycle. The two monitored cracks exhibited small micrometer-scale fluctuations associated with temperature variation and showed no cumulative widening trend during the observation period. This study demonstrates the feasibility of campaign-based tunnel crack monitoring using a trustworthy edge-sensing architecture, while longer deployments with more sensing nodes are needed to fully evaluate scalability and operational durability.

Structural health monitoring (SHM) systems based on Internet of Things (IoT) technologies have become an effective approach for continuous monitoring of bridge infrastructures. However, many wireless monitoring systems relying on LoRaWAN or contention-based communication suffer from packet collisions, unpredictable latency, and limited scalability when multiple sensing nodes operate simultaneously. To address these limitations, this study proposes a soft real-time LoRa-based IoT architecture for bridge SHM using a time division multiple access (TDMA) communication framework implemented on an embedded real-time platform. The proposed system integrates distributed vibration sensing nodes, a TDMA-enabled LoRa communication layer, an ESP32-based gateway, and a web-based monitoring database for remote visualization and analysis. The architecture leverages FreeRTOS (v10.4.3) for system-level task scheduling, enabling concurrent execution of sensing, communication, and networking processes across the dual-core ESP32-WROOM-32D platform. Experimental results obtained using a laboratory-scale cable-stayed bridge model demonstrate stable multi-node communication with a packet delivery ratio exceeding 95% and predictable TDMA-scheduled transmission cycles with TDMA slots of 100–200 ms under the evaluated operating conditions. The experiments validate end-to-end operation using a representative three-node deployment, while broader scalability is evaluated analytically through the TDMA capacity model and identified as future work for larger physical deployments.

Rutting and moisture-induced damage are critical challenges for asphalt pavements in tropical regions such as Vietnam, where high temperatures and heavy traffic accelerate permanent deformation. This study aims to evaluate the rutting performance, deformation uniformity, and stripping resistance of conventional and dry-process SBS-modified asphalt mixtures using a comprehensive Hamburg Wheel Tracking (HWT) database of 212 specimens (74 conventional and 138 SBS-modified) collected from 2021 to 2025. The analysis incorporates both laboratory-prepared and field-extracted specimens with different aggregate types and gradations, tested under submerged conditions at 50 °C and 60 °C with loading up to 40,000 passes. The results show that dry-process SBS modification significantly improves rutting resistance, reducing rut depth by 42% at 20,000 passes. SBS mixtures maintained high stability even at 40,000 passes, with a mean rut depth of 5.60 ± 0.95 mm. The Stripping Inflection Point (SIP) is substantially delayed in SBS mixtures (approximately 17,800–20,300 passes) compared to conventional mixtures (~9000 passes), with 63% of SBS samples exhibiting no stripping within the 40,000-pass protocol, indicating enhanced resistance to moisture damage. In addition, SBS-modified mixtures exhibit improved deformation uniformity and benefit from coarser aggregate gradations. Overall, the findings demonstrate that dry-process SBS modification provides a reliable and practical solution for enhancing asphalt pavement performance under high-temperature and heavy-traffic conditions in Vietnam.